Wireless transmitting and receiving test system and method

Abstract

The invention relates to the technical field of wireless transmitting and receiving testing, in particular to a wireless transmitting and receiving testing system and method. The method comprises the following steps: performing spectral range setting on a standard RX to obtain spectral range configuration data; environment noise sampling is carried out according to the spectral range configuration data, noise feature extraction is carried out, and environment noise feature vectors are obtained; performing environmental noise spectrum generation on the environmental noise feature vector and the spectral range configuration data to obtain an environmental noise feature spectrum; performing dynamic threshold calculation according to the environmental noise characteristic spectrum and a preset signal-to-noise ratio margin parameter to obtain a self-adaptive TX power threshold; and performing TX signal intensity acquisition on the TX to be measured to obtain actually measured TX signal intensity data. According to the invention, through the wireless transmitting and receiving test technology, the cost of test equipment and manpower is reduced, the test speed is accelerated, and the test efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless transmission and reception testing, and particularly to a wireless transmission and reception testing system and method. Background Technique

[0002] During the production process of U-segment microphone products, it is necessary to ensure that the transmission power of TX and the reception sensitivity of RX meet the design requirements. However, traditional wireless testing equipment (such as spectrum analyzers, signal generators, etc.) is expensive and complex to operate. Usually, professional operators and complex calibration steps are required, which are time-consuming and laborious, and are a significant burden for cost-sensitive PCBA factories and complete machine factories.

[0003] Traditional testing methods based on fixed thresholds are easily affected by environmental noise and device individual differences, and the test results are not intuitive, resulting in inaccurate test results and even misjudgments. Summary of the Invention

[0004] Based on this, it is necessary to provide a wireless transmission and reception testing system and method to solve at least one of the above technical problems.

[0005] To achieve the above object, a wireless transmission and reception testing method includes the following steps:

[0006] Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; sample the environmental noise according to the spectrum range configuration data, extract the noise characteristics, and obtain the environmental noise feature vector; generate the environmental noise spectrum based on the environmental noise feature vector and the spectrum range configuration data to obtain the environmental noise feature spectrum;

[0007] Step S2: Calculate the dynamic threshold according to the environmental noise feature spectrum and the preset signal-to-noise ratio margin parameter to obtain the adaptive TX power threshold; collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; perform dynamic determination on the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record;

[0008] Step S3: Set the starting power and step through the upper computer to obtain the gradient scan configuration parameters; monitor the reception status of the RX under test to obtain the reception status monitoring instruction; perform RX sensitivity testing on the RX under test according to the reception status monitoring instruction to obtain the original RX sensitivity data; generate the sensitivity gradient curve based on the original RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record;

[0009] Step S4: Perform statistical analysis of performance parameters on the TX power test data records and RX sensitivity test data records, and conduct quality trend assessment to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on the performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; perform parameter update and application on the self-optimized test parameter set to obtain a parameter update instruction; perform structured encapsulation of the test data on the self-optimized test parameter set to obtain a structured test data packet; use the abnormal alarm information, parameter update instruction, and structured test data packet to implement the wireless transmission and reception test task.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: The host computer sets the spectrum range for the standard RX to obtain spectrum range configuration data;

[0012] Step S12: The standard RX samples the ambient noise according to the spectrum range configuration data to obtain raw noise sampling data; perform noise data preprocessing on the raw noise sampling data to obtain preprocessed noise data;

[0013] Step S13: The host computer receives the preprocessed noise data from the standard RX, extracts the noise characteristics to obtain an ambient noise feature vector; the host computer sends self-calibration instructions to the standard TX and the standard RX respectively to obtain self-calibration configuration instructions;

[0014] Step S14: The standard TX performs transmitter loopback calibration according to the self-calibration configuration instructions to obtain transmission calibration parameters; the standard RX performs receiver loopback calibration according to the self-calibration configuration instructions to obtain reception calibration parameters;

[0015] Step S15: The host computer generates an ambient noise spectrum from the ambient noise feature vector and the spectrum range configuration data to obtain an ambient noise feature spectrum.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: The host computer extracts the band noise floor from the ambient noise feature spectrum to obtain band noise floor data; calculate the minimum received power according to the band noise floor data and the preset signal-to-noise ratio margin parameter to obtain the minimum received power threshold;

[0018] Step S22: Obtain the standard RX specification parameters; perform standard receiver sensitivity compensation according to the standard RX specification parameters to obtain the receiver sensitivity compensation value;

[0019] Step S23: Perform dynamic reverse deduction of the transmission power threshold based on the receiver sensitivity compensation value and the minimum received power threshold to obtain the adaptive TX power threshold;

[0020] Step S24: The upper computer starts the TX under test to obtain a TX transmission instruction for the TX under test; the standard RX collects the signal strength according to the TX transmission instruction for the TX under test to obtain the measured TX signal strength data;

[0021] Step S25: The upper computer makes a dynamic determination of the measured TX signal strength data and the adaptive TX power threshold to obtain the TX power test result;

[0022] Step S26: Store the test data of the adaptive TX power threshold, the measured TX signal strength data, and the TX power test result to obtain the TX power test data record.

[0023] Preferably, step S3 includes the following steps:

[0024] Step S31: Set the starting power and step through the upper computer to obtain the gradient scan configuration parameters;

[0025] Step S32: Set the standard transmitter power for the standard TX according to the spectrum range configuration data to obtain the current power value of the standard TX; monitor the receiving state of the RX under test to obtain the receiving state monitoring instruction;

[0026] Step S33: Perform RX sensitivity testing on the RX under test based on the current power value of the standard TX and the receiving state monitoring instruction to obtain the original RX sensitivity data;

[0027] Step S34: Generate a sensitivity gradient curve based on the original RX sensitivity data to obtain the RX sensitivity gradient curve;

[0028] Step S35: Store the test data of the gradient scan configuration parameters, the original RX sensitivity data, and the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

[0029] Preferably, step S33 includes the following steps:

[0030] Step S331: Collect gradient point data for the RX under test based on the current power value of the standard TX and the receiving state monitoring instruction to obtain single-point gradient test data;

[0031] Step S332: Perform power step attenuation control based on the gradient scan configuration parameters and the current power value of the standard TX to obtain the next power value of the standard TX;

[0032] Step S333: Judge the scan termination condition according to the gradient scan configuration parameters, the next power value of the standard TX and the single-point gradient test data to obtain the scan termination flag;

[0033] Step S334: Integrate the gradient data of the single-point gradient test data according to the scan termination flag to obtain the original RX sensitivity data.

[0034] Preferably, step S34 includes the following steps:

[0035] Step S341: Extract the power-RSSI data pairs from the original RX sensitivity data and generate the power-RSSI curve data to obtain the power RSSI curve data point set;

[0036] Step S342: Extract the power-PER / BER data pairs from the original RX sensitivity data and generate the power-PER / BER curve data to obtain the power error rate curve data point set;

[0037] Step S343: Judge the sensitivity threshold power value for the power error rate curve data point set to obtain the sensitivity threshold power value;

[0038] Step S344: Generate the sensitivity gradient curve for the power RSSI curve data point set and the power error rate curve data point set according to the sensitivity threshold power value and perform threshold marking to obtain the RX sensitivity gradient curve.

[0039] Preferably, step S4 includes the following steps:

[0040] Step S41: The upper computer loads the historical test data of the environmental noise characteristic spectrum, the TX power test data record and the RX sensitivity test data record to form a historical test data set;

[0041] Step S42: Perform statistical analysis of the performance parameters on the historical test data set to obtain the performance parameter statistics; evaluate the quality trend according to the performance parameter statistics to obtain the quality trend index;

[0042] Step S43: Dynamically optimize and adjust the adaptive TX power threshold according to the performance parameter statistics, the quality trend index and the environmental noise characteristic spectrum to obtain the optimized dynamic TX power threshold;

[0043] Step S44: Perform gradient step self-optimization adjustment on the gradient scan configuration parameters according to the performance parameter statistics and the quality trend index to obtain the optimized gradient scan step value;

[0044] Step S45: Generate a self-optimizing parameter set for the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimizing test parameter set;

[0045] Step S46: The host computer performs structured encapsulation of test data on the environmental noise characteristic spectrum, transmission calibration parameters, reception calibration parameters, TX power test data records, RX sensitivity test data records, and the self-optimizing test parameter set to obtain a structured test data packet.

[0046] Preferably, step S43 includes the following steps:

[0047] Step S431: Read the current dynamic TX power threshold for the adaptive TX power threshold to obtain the current dynamic threshold;

[0048] Step S432: Evaluate the recent TX power test yield rate for the performance parameter statistic to obtain the recent TX yield rate;

[0049] Step S433: Calculate the threshold adjustment amount based on the yield rate deviation for the recent TX yield rate and the preset yield rate target value to obtain the yield rate deviation adjustment amount;

[0050] Step S434: Obtain the recent environmental noise level change for the quality trend index and the environmental noise characteristic spectrum to obtain the recent noise change amount;

[0051] Step S435: Calculate the threshold adjustment amount based on the noise change according to the recent noise change amount to obtain the noise change adjustment amount;

[0052] Step S436: Calculate the comprehensive adjustment amount for the yield rate deviation adjustment amount and the noise change adjustment amount to obtain the total adjustment amount;

[0053] Step S438: Adjust the dynamic TX power threshold according to the total adjustment amount and the current dynamic threshold to obtain the optimized dynamic TX power threshold.

[0054] Preferably, step S44 includes the following steps:

[0055] Step S441: Evaluate the recent volatility of RX sensitivity test data for the performance parameter statistic to obtain the recent sensitivity volatility;

[0056] Step S442: Evaluate the product RX sensitivity performance stability for the quality trend index to obtain the RX performance stability;

[0057] Step S443: Calculate the step value adjustment amount based on the volatility according to the recent sensitivity volatility to obtain the volatility adjustment amount;

[0058] Step S444: Calculate the step value adjustment amount based on performance stability according to the RX performance stability to obtain the stability adjustment amount;

[0059] Step S445: Calculate the comprehensive step value adjustment amount for the volatility adjustment amount and the stability adjustment amount to obtain the comprehensive step value adjustment amount;

[0060] Step S446: Obtain the current step value; perform gradient scan step value adjustment based on the comprehensive step value adjustment amount and the current step value to obtain the optimized gradient scan step value.

[0061] Preferably, the present invention further provides a wireless transmission and reception test system for performing the wireless transmission and reception test method described above. The wireless transmission and reception test system includes:

[0062] A system initialization module for setting the spectrum range of the standard RX to obtain spectrum range configuration data; performing environmental noise sampling according to the spectrum range configuration data, and performing noise feature extraction to obtain an environmental noise feature vector; generating an environmental noise spectrum for the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum;

[0063] A transmission power adaptive test module for calculating a dynamic threshold according to the environmental noise feature spectrum and a preset signal-to-noise ratio margin parameter to obtain an adaptive TX power threshold; collecting the TX signal strength of the TX under test to obtain measured TX signal strength data; performing dynamic determination on the measured TX signal strength data and the adaptive TX power threshold, and storing the test data to obtain a TX power test data record;

[0064] A receiving sensitivity gradient scan module for setting the starting power and step through the upper computer to obtain gradient scan configuration parameters; monitoring the receiving state of the RX under test to obtain a receiving state monitoring instruction; performing RX sensitivity test on the RX under test according to the receiving state monitoring instruction to obtain RX sensitivity raw data; generating a sensitivity gradient curve according to the RX sensitivity raw data to obtain an RX sensitivity gradient curve; storing the test data according to the RX sensitivity gradient curve to obtain an RX sensitivity test data record;

[0065] A threshold self-optimization module is used to perform performance parameter statistical analysis on TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on the performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; perform parameter update and application on the self-optimized test parameter set to obtain a parameter update instruction; perform structured encapsulation of test data on the self-optimized test parameter set to obtain a structured test data packet; use the abnormal alarm information, parameter update instruction, and structured test data packet to implement wireless transmission and reception test tasks.

[0066] The present invention realizes the effective characterization of the test environment noise by setting the spectrum range of the standard RX, sampling the ambient noise, extracting the noise characteristics, and generating the ambient noise spectrum. Its beneficial effects are as follows: it can accurately obtain the noise spectrum distribution of the test environment, providing an important ambient noise benchmark for subsequent adaptive threshold calculation and dynamic test determination. The generation of the ambient noise characteristic spectrum enables the test system to perceive and adapt to different test environment noise levels, avoiding the misjudgment problem caused by ambient noise fluctuations in the traditional fixed-threshold test method, and improving the accuracy and environmental adaptability of wireless device testing. At the same time, the ambient noise spectrum data also provides a necessary data basis for subsequent quality trend analysis and test parameter self-optimization. According to the ambient noise characteristic spectrum generated in step S1 and combined with the preset signal-to-noise ratio margin parameter, an adaptive TX power threshold is calculated dynamically. This adaptive threshold can be adjusted dynamically according to the actual ambient noise level, making the TX power test closer to the real application scenario. By collecting the TX signal strength of the TX under test and making a dynamic determination with the adaptive TX power threshold, an accurate assessment of the transmit power is achieved. Its beneficial effects are as follows: it can effectively reduce the influence of ambient noise on the TX power test result, avoiding misjudging a qualified transmitter as unqualified in a high-noise environment or misjudging an unqualified transmitter as qualified in a low-noise environment, improving the reliability and accuracy of the TX power test, and providing reliable TX power test data records for subsequent quality control. Through the upper computer for gradient scan configuration and performing RX sensitivity testing on the RX under test, an RX sensitivity gradient curve is generated. Its beneficial effects are as follows: it can comprehensively and finely evaluate the receiving sensitivity performance of the RX under test. The gradient scan method can detail the performance of the RX at different receiving powers, and through the power-RSSI curve and power-PER curve, visually display the receiving sensitivity characteristics and packet reception reliability of the RX. The generation of the sensitivity gradient curve makes the test result easier to analyze and understand, and can accurately determine the sensitivity threshold value of the RX. The storage of the RX sensitivity test data record provides detailed RX sensitivity performance data support for subsequent quality analysis, performance evaluation, and self-optimization parameter adjustment. By performing performance parameter statistical analysis and quality trend assessment on the TX power test data record and the RX sensitivity test data record, performance parameter statistics and quality trend indicators are obtained. Its beneficial effects are as follows: it can achieve the comprehensive monitoring of the wireless performance of the product and the effective control of the quality trend. Through the detection of abnormal test results, abnormal situations in the test process can be found in time and alarms can be given to ensure the test quality. The dynamic threshold self-optimization adjustment and gradient step self-optimization adjustment enable the test system to automatically optimize the test parameters according to historical test data and quality trends, improving the test efficiency and test accuracy, and reducing manual intervention. The generation, update, and application of the self-optimization parameter set realize the intelligence and self-adaptability of the test system.The generation of structured test data packets facilitates the storage, transmission, and reuse of test data, providing strong support for the automation and intelligence of wireless transmission and reception test tasks, and ultimately improving the overall efficiency and quality assurance level of wireless product testing. Therefore, the present invention provides a wireless transmission and reception test method, which reduces the test equipment and labor costs by using standard TX / RX and host computer software, as well as a simplified test process. Functions such as dynamic threshold calculation, real-time RSSI monitoring, and color identification display speed up the test speed and improve the test efficiency. The dynamic threshold calculation and environmental noise compensation mechanism effectively reduce the influence of environmental noise and device individual differences on the test results, improving the test accuracy and reliability. A low-cost, high-efficiency, high-precision, and intelligent wireless transmission and reception test solution is provided, solving the disadvantages of expensive test equipment, cumbersome processes, and inaccurate and unintuitive fixed threshold test methods in existing test methods. Brief Description of the Drawings

[0067] Figure 1 It is a schematic diagram of the step flow of a wireless transmission and reception test method;

[0068] Figure 2 It is a schematic diagram of the detailed implementation step flow of step S4 in the present invention.

[0069] The realization of the purpose, functional characteristics, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0070] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0072] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve the above object, please refer to Figures 1 to 2 , a wireless transmission and reception test method, comprising the following steps:

[0074] Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; perform environmental noise sampling according to the spectrum range configuration data, and then perform noise feature extraction to obtain an environmental noise feature vector; generate an environmental noise spectrum for the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum;

[0075] Step S2: Calculate a dynamic threshold according to the environmental noise feature spectrum and a preset signal-to-noise ratio margin parameter to obtain an adaptive TX power threshold; collect the TX signal strength of the TX under test to obtain measured TX signal strength data; perform a dynamic determination on the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain a TX power test data record;

[0076] Step S3: Set the starting power and step size through the upper computer to obtain gradient scan configuration parameters; monitor the reception status of the RX under test to obtain a reception status monitoring instruction; perform an RX sensitivity test on the RX under test according to the reception status monitoring instruction to obtain raw RX sensitivity data; generate a sensitivity gradient curve according to the raw RX sensitivity data to obtain an RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain an RX sensitivity test data record;

[0077] Step S4: Perform statistical analysis of performance parameters on the TX power test data record and the RX sensitivity test data record, and conduct a quality trend assessment to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on the performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; perform parameter update and application on the self-optimized test parameter set to obtain a parameter update instruction; perform structured encapsulation of test data on the self-optimized test parameter set to obtain a structured test data packet; use the abnormal alarm information, parameter update instruction, and structured test data packet to implement the wireless transmission and reception test task.

[0078] In the embodiment of the present invention, with reference to Figure 1 as shown, it is a schematic diagram of the step flow of the wireless transmission and reception test method of the present invention. In this example, the wireless transmission and reception test method includes the following steps:

[0079] Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; perform environmental noise sampling according to the spectrum range configuration data, and perform noise feature extraction to obtain an environmental noise feature vector; generate an environmental noise spectrum from the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum.

[0080] In the embodiment of the present invention, first, the host computer receives the spectrum range configuration through the user interface and sends parameters such as the start frequency, end frequency, and step value to the standard RX in the form of serial port instructions to complete the spectrum range setting. The standard RX performs environmental noise sampling based on the received spectrum range configuration, performs multiple RSSI samplings at each frequency point and takes the average to form the original noise sampling data, and transmits it to the host computer through the serial port. The host computer preprocesses the received original noise sampling data, including outlier removal and moving average filtering, to obtain preprocessed noise data. The host computer performs frequency domain analysis on the preprocessed noise data, such as FFT transformation, to extract the noise feature vector, including statistical features and frequency domain information such as the average noise power, the standard deviation of the noise power, and the average power value of the main noise frequency band. At the same time, the host computer sends self-calibration instructions to the standard TX and the standard RX, and the standard TX and the standard RX respectively perform transmitter loopback calibration and receiver loopback calibration to obtain their respective calibration parameters. Finally, the host computer integrates the environmental noise feature vector and the spectrum range configuration data to generate an environmental noise feature spectrum and displays it on the user interface in the form of a spectrum diagram and a data table.

[0081] Step S2: Calculate the dynamic threshold according to the environmental noise characteristic spectrum and the preset signal-to-noise ratio margin parameter to obtain the adaptive TX power threshold; collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record;

[0082] In the embodiment of the present invention, the host computer first loads the environmental noise characteristic spectrum generated in step S1, extracts the noise power spectral density data within the working frequency band of the U-segment microphone under test, and calculates the average noise power of this frequency band as the frequency band noise base data. The system reads the preset signal-to-noise ratio margin parameter, and calculates the minimum received power threshold in combination with the frequency band noise base data. The system obtains the specification parameters of the standard RX, and inversely calculates the adaptive TX power threshold according to the sensitivity compensation value of the standard RX and the minimum received power threshold. This threshold takes into account the environmental noise and receiver characteristics. The host computer sends a transmission instruction to the TX under test to start the TX under test to transmit a signal. The standard RX collects the measured TX signal strength data of the TX under test and transmits the data to the host computer. The host computer statistically analyzes the measured TX signal strength data, calculates the average measured TX signal strength, and dynamically determines it with the adaptive TX power threshold to obtain the TX power test result (PASS / NG). Finally, the system stores information such as the adaptive TX power threshold, the measured TX signal strength data, and the TX power test result in the local test database to generate the TX power test data record.

[0083] Step S3: Set the starting power and step through the upper computer to obtain the gradient scan configuration parameters; monitor the receiving state of the RX under test to obtain the receiving state monitoring instruction; perform the RX sensitivity test on the RX under test according to the receiving state monitoring instruction to obtain the original RX sensitivity data; generate a sensitivity gradient curve according to the original RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record;

[0084] In the embodiments of the present invention, a tester sets the starting power, power step value, and scan termination condition of the gradient scan through the upper computer user interface to form gradient scan configuration parameters. The upper computer sets the operating frequency of the standard TX according to the spectrum range configuration data, sets the starting transmission power of the standard TX according to the gradient scan configuration parameters, and simultaneously sends a receive status monitoring instruction to the RX under test. At each power gradient point, the standard TX maintains the current power transmission, and the RX under test collects receive status data, including RSSI and PER values, and transmits the data to the upper computer, which records it as single-point gradient test data. The upper computer attenuates the transmission power of the standard TX according to the power step value and repeats the data collection process until the scan termination condition is met. After the scan is completed, the upper computer integrates all the single-point gradient test data to form the original RX sensitivity data. Based on the original RX sensitivity data, the upper computer generates a power-RSSI curve and a power-PER curve, and marks the sensitivity threshold power value on the power-PER curve to form the RX sensitivity gradient curve. Finally, the system stores information such as the gradient scan configuration parameters, the original RX sensitivity data, and the RX sensitivity gradient curve in the local test database to generate an RX sensitivity test data record.

[0085] Step S4: Perform statistical analysis of performance parameters on the TX power test data record and the RX sensitivity test data record, and conduct a quality trend assessment to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on the performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized test parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value; perform parameter update and application on the self-optimized test parameter set to obtain a parameter update instruction; perform structured encapsulation of test data on the self-optimized test parameter set to obtain a structured test data packet; use the abnormal alarm information, parameter update instruction, and structured test data packet to implement the wireless transmission and reception test task.

[0086] In the embodiments of the present invention, the host computer loads historical test data, including environmental noise characteristic spectra, TX power test data records, and RX sensitivity test data records, to form a historical test data set. The system performs statistical analysis on the performance parameters of the historical test data set, and calculates statistics such as the TX power PASS rate, RX sensitivity threshold value, and environmental noise power. Based on the performance parameter statistics, the system conducts a quality trend assessment to obtain quality trend indicators, such as the PASS rate trend, sensitivity threshold control chart, etc. The system detects abnormal test results according to the performance parameter statistics and quality trend indicators, and generates abnormal alarm information. The system dynamically adjusts the adaptive TX power threshold according to the recent TX yield deviation and recent changes in the environmental noise level, and obtains the optimized dynamic TX power threshold. The system dynamically adjusts the gradient scan step value according to the recent volatility of the RX sensitivity test data and the RX performance stability, and obtains the optimized gradient scan step value. The system integrates the optimized dynamic TX power threshold and the optimized gradient scan step value into a self-optimizing test parameter set. The system updates and applies the parameters of the self-optimizing test parameter set, and structurally encapsulates all test data including environmental noise characteristic spectra, calibration parameters, test data records, and the self-optimizing test parameter set to obtain a structured test data packet for subsequent wireless transmission and reception test tasks.

[0087] Preferably, step S1 includes the following steps:

[0088] Step S11: The host computer sets the spectrum range for the standard RX to obtain spectrum range configuration data;

[0089] Step S12: The standard RX samples the environmental noise according to the spectrum range configuration data to obtain raw noise sampling data; the raw noise sampling data is preprocessed for noise data to obtain preprocessed noise data;

[0090] Step S13: The host computer receives the preprocessed noise data from the standard RX, extracts noise characteristics, and obtains an environmental noise feature vector; the host computer sends self-calibration instructions to the standard TX and the standard RX respectively to obtain self-calibration configuration instructions;

[0091] Step S14: The standard TX performs transmitter loopback calibration according to the self-calibration configuration instructions to obtain transmission calibration parameters; the standard RX performs receiver loopback calibration according to the self-calibration configuration instructions to obtain reception calibration parameters;

[0092] Step S15: The host computer generates an environmental noise spectrum from the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise characteristic spectrum.

[0093] In the embodiment of the present invention, the host computer first starts the test control program, and the user interface of the test control program presents the spectrum range configuration option. The tester inputs the working frequency band range of the U-segment microphone to be tested through the user interface. For example, the starting frequency is set to 470 MHz, and the ending frequency is set to 960 MHz. The frequency step value is set to 1 MHz, and this step value determines the resolution of the spectrum scan. The host computer encodes these configuration parameters, including the starting frequency, the ending frequency, and the frequency step value, into control instructions that conform to the standard RX communication protocol. For example, a predefined binary data packet format is adopted, the data packet header contains the instruction type identifier, and the data packet body contains the encoded spectrum range parameters. The host computer sends this control instruction to the MCU of the standard RX in the form of serial communication through the USB-to-serial port module. After receiving and parsing the instruction, the MCU extracts the spectrum range parameters and stores these parameters in the memory of the standard RX for subsequent environmental noise spectrum scanning operations. At this time, the spectrum range configuration data is generated and stored in the standard RX and the host computer system, providing a parameter basis for subsequent environmental scanning.

[0094] After receiving the spectrum range configuration data, the MCU of the standard RX controls its internal radio frequency front end to enter the spectrum scan mode. The frequency synthesizer of the standard RX starts scanning according to the set starting frequency, for example, 470 MHz. At each scanning frequency point, for example, 470 MHz, 471 MHz, 472 MHz up to 960 MHz, the radio frequency receiving circuit of the standard RX performs multiple rapid RSSI (Received Signal Strength Indication) samplings. For example, 10 samplings are performed at each frequency point. The MCU performs an arithmetic average on these 10 sampling values to reduce the interference of random noise and obtains the average RSSI value of this frequency point. The standard RX sends the average RSSI value of each frequency point and the corresponding frequency information to the host computer in accordance with a predefined serial port data frame format, for example, the CSV format or the JSON format, through the serial port. After receiving the original noise sampling data, the host computer first performs an outlier rejection operation. For example, the 3σ principle is adopted to reject the data points that are significantly deviated from the average RSSI value of this frequency point by more than 3 standard deviations. These data points are caused by instantaneous strong interference. Subsequently, the host computer performs a moving average filtering on the RSSI data after outlier rejection. For example, a 5-point moving average filter is adopted to further smooth the noise data, reduce the influence of random noise, obtain a more stable preprocessed noise data, and store it in the host computer memory for subsequent noise feature extraction.

[0095] After the host computer receives the preprocessed noise data, the program starts the noise feature extraction module. The noise feature extraction module first performs frequency-domain analysis on the preprocessed noise data. For example, using the Fast Fourier Transform (FFT) algorithm, it converts the RSSI data in the time domain to the frequency domain to obtain the power spectral density distribution of the noise. By analyzing the power spectral density, the main frequency bands and frequency components of the environmental noise are identified. In addition, the noise feature extraction module also calculates the statistical features of the preprocessed noise data. For example, within the set working frequency band range of the U-section microphone, it calculates statistics such as the average noise power, the standard deviation of the noise power, and the maximum noise power value. These statistics and the frequency-domain analysis results are combined into an environmental noise feature vector. For example, a data vector containing the average noise power, the standard deviation of the noise power, and the average power values of several main noise frequency bands. The host computer then generates a self-calibration configuration instruction, which contains the target transmit power value of the standard TX, such as 10 dBm, and the target receive sensitivity value of the standard RX, such as -95 dBm, and sends this self-calibration configuration instruction to the MCUs of the standard TX and the standard RX respectively through the serial port to start the self-calibration process of the standard devices.

[0096] After the MCU of the standard TX receives the self-calibration configuration instruction, it controls the internal RF switch to switch to the loopback calibration path, couples the output terminal of the power amplifier (PA) to the input terminal of the low-noise amplifier (LNA) through an attenuator to form an internal closed loop. The MCU controls the digital attenuator to adjust the transmit power and uses the internal power detector to measure the receive power on the loopback path. Through the PID closed-loop control algorithm, the MCU continuously adjusts the gain of the PA and the attenuation value of the digital attenuator to make the actual transmit power gradually approach the target transmit power value set in the self-calibration configuration instruction, such as 10 dBm. When the transmit power stabilizes near the target value and the error is within the allowable range, such as ±0.1 dBm, the transmitter loopback calibration is completed. The MCU stores the PA gain calibration parameter and the calibration parameter of the digital attenuator at this time in the non-volatile memory of the standard TX as the transmit calibration parameter.

[0097] After the MCU of the standard RX receives the self-calibration configuration instruction, it controls the internal signal generator to generate a calibration signal with a known power, such as -60 dBm, and injects this signal into the input end of the LNA to form a receiver loopback calibration path. The MCU controls the digital attenuator to adjust the received signal strength and monitors the RSSI value and bit error rate (BER, if applicable) of the received signal. By adjusting the gain of the LNA and the attenuation value of the digital attenuator, the receiving sensitivity of the standard RX gradually approaches the target sensitivity value set in the self-calibration configuration instruction, such as -95 dBm. When the receiving sensitivity reaches the target value and the BER is lower than the preset threshold, the receiver loopback calibration is completed. The MCU stores the LNA gain calibration parameter and the calibration parameter of the digital attenuator at this time in the non-volatile memory of the standard RX as the receive calibration parameter.

[0098] After the host computer receives the environmental noise feature vector sent by the standard RX and the spectrum range configuration data set in step S11, it starts the environmental noise spectrum generation module. The environmental noise spectrum generation module integrates each characteristic parameter in the environmental noise feature vector, such as average noise power, noise power standard deviation, average power value of the main noise frequency band, etc., with the frequency range information in the spectrum range configuration data. The host computer program generates an environmental noise spectrum diagram based on the integrated data. The spectrum diagram uses frequency as the horizontal axis and noise power spectral density as the vertical axis, and is visually displayed on the user interface. Different colors or line types can be used in the spectrum diagram to distinguish the noise intensity levels of different frequency bands. At the same time, the host computer program also displays the numerical characteristic parameters in the environmental noise feature vector, such as the average noise power value, in the form of a data table on the user interface for the convenience of testers to view.

[0099] Preferably, step S2 includes the following steps:

[0100] Step S21: The upper computer extracts the band noise floor of the environmental noise characteristic spectrum to obtain the band noise floor data; calculates the minimum received power according to the band noise floor data and the preset signal-to-noise ratio margin parameter to obtain the minimum received power threshold;

[0101] Step S22: Obtain the standard RX specification parameters; perform standard receiver sensitivity compensation according to the standard RX specification parameters to obtain the receiver sensitivity compensation value;

[0102] Step S23: Perform dynamic transmit power threshold back-calculation according to the receiver sensitivity compensation value and the minimum received power threshold to obtain the adaptive TX power threshold;

[0103] Step S24: The upper computer starts the TX under test to obtain the TX under test transmit instruction; the standard RX collects the signal strength according to the TX under test transmit instruction to obtain the measured TX signal strength data;

[0104] Step S25: The upper computer dynamically determines the measured TX signal strength data and the adaptive TX power threshold to obtain the TX power test result;

[0105] Step S26: Store the test data of the adaptive TX power threshold, the measured TX signal strength data, and the TX power test result to obtain the TX power test data record.

[0106] In the embodiment of the present invention, the upper computer program first loads the environmental noise characteristic spectrum data generated in step S15. The program analyzes this spectrum data and, according to the preset operating frequency band of the U-section microphone to be measured, for example, a frequency band with a center frequency of 500 MHz and a bandwidth of 20 MHz. In the environmental noise characteristic spectrum, the program extracts the noise power spectral density data within this 20 MHz bandwidth range. To obtain the frequency band noise floor data, the program calculates the average value of the noise power spectral density at all frequency points within this frequency band and converts the average value to the dBm unit as the noise floor power level of this frequency band. Subsequently, the program reads the preset signal-to-noise ratio (SNR) margin parameter. For example, the SNR margin is set to 20 dB, and this parameter represents the degree to which the expected received signal power exceeds the noise floor. The minimum received power threshold is calculated by adding the frequency band noise floor data and the SNR margin parameter. For example, if the frequency band noise floor is -90 dBm and the SNR margin is 20 dB, then the minimum received power threshold is calculated as -70 dBm. The calculated minimum received power threshold is stored in the upper computer memory as the basic data for subsequent dynamic transmit power threshold calculation.

[0107] The upper computer program obtains the specification parameters of the standard RX from the preset database or configuration file. These specification parameters include the nominal receiving sensitivity index of the standard RX. For example, at a specific bit error rate (BER), the minimum signal power value that the standard RX can reliably receive, assuming the nominal sensitivity is -95 dBm. To compensate for the sensitivity deviation of the standard RX in actual applications, a receiver sensitivity compensation mechanism is introduced. The compensation value is determined according to the calibration report or historical test data of the standard RX. For example, if the calibration data shows that the actual sensitivity of the standard RX is 2 dB worse than the nominal value, then the receiver sensitivity compensation value is set to +2 dB. If the actual sensitivity of the standard RX is better than the nominal value, the compensation value can be set to a negative value or zero. The receiver sensitivity compensation value is used for subsequent dynamic transmit power threshold back-calculation to ensure the accuracy of the test results, and the compensation value is stored in the upper computer memory for subsequent step calls.

[0108] The host computer program receives the minimum received power threshold calculated in step S21, e.g., -70 dBm, and the receiver sensitivity compensation value determined in step S22, e.g., +2 dB. The reverse calculation process of the dynamic transmit power threshold is as follows: First, add the receiver sensitivity compensation value to the minimum received power threshold to obtain the compensated minimum received power threshold. For example, -70 dBm + 2 dB = -68 dBm. Considering the fluctuations in the actual test environment and ensuring the test margin, add a safety margin, e.g., 3 dB, to the compensated minimum received power threshold. Therefore, the adaptive TX power threshold is finally calculated as the compensated minimum received power threshold plus the safety margin. For example, -68 dBm + 3 dB = -65 dBm. This adaptive TX power threshold represents the minimum transmit power level that the TX under test needs to achieve under the current ambient noise conditions to ensure that the standard RX can reliably receive the signal and meet the preset SNR requirements. The calculated adaptive TX power threshold is stored in the host computer memory for subsequent dynamic determination of the TX power.

[0109] The host computer program generates a transmit instruction for the TX under test, which contains parameters such as the operating frequency and modulation mode of the TX under test. For example, the operating frequency is set to 500 MHz and the modulation mode is set to GFSK. The host computer sends this transmit instruction to the MCU of the TX under test in a serial communication manner through a USB-to-serial port module. After receiving and parsing the transmit instruction, the MCU of the TX under test controls its radio frequency transmit circuit to start the transmit function and continuously transmit radio frequency signals according to the operating frequency and modulation mode set in the instruction. At the same time, the standard RX is in the receive mode and continuously monitors the radio frequency signals transmitted by the TX under test. After the radio frequency receive front-end circuit of the standard RX receives the signal of the TX under test, it demodulates the signal and measures the RSSI (Received Signal Strength Indication) value. The MCU of the standard RX periodically reads the value of the RSSI register and converts the RSSI value into the actual received power value (unit: dBm). The conversion relationship is determined according to the calibration data or specification of the standard RX. The standard RX sends the converted received power value to the host computer in real time through the serial port. The host computer program receives and caches the received power data sent by the standard RX, continuously collects the received power data for a period of time, e.g., 5 seconds, to provide a data basis for subsequent signal strength evaluation.

[0110] After the host computer program receives the measured TX signal strength data collected and sent by the standard RX, it first statistically analyzes the collected multi-point received power data. For example, calculate the average value of the received power data during this period to eliminate the influence of instantaneous fluctuations and obtain the average measured TX signal strength. Then, the host computer program compares this average measured TX signal strength with the adaptive TX power threshold calculated in step S23. The logic for dynamic determination is as follows: If the average measured TX signal strength is greater than or equal to the adaptive TX power threshold, it is determined that the transmit power of the TX under test is qualified (PASS). If the average measured TX signal strength is less than the adaptive TX power threshold, it is determined that the transmit power of the TX under test is unqualified (NG). To improve the robustness of the determination, a small determination margin can be set. For example, only when the average measured TX signal strength is significantly higher than the threshold, such as higher than 3 dB, is it determined as PASS. The final TX power test result (PASS or NG) is recorded in the host computer memory and used for user interface display and test report generation.

[0111] After completing the dynamic determination of the TX power, the host computer program starts the test data storage module. This module integrates the key data items of this TX power test, including the adaptive TX power threshold calculated in step S23, the measured TX signal strength data collected in step S24 (such as the average value, maximum value, minimum value, etc.), and the TX power test result (PASS or NG) obtained in step S25, as well as the test timestamp, the unique identifier of the TX under test (such as the serial number SN code), etc., to form a complete TX power test data record. The host computer program stores this data record into the local test database according to a predefined database schema or data format, such as in a structured JSON format or CSV format. The database can be a relational database such as MySQL or a non-relational database such as MongoDB, and the appropriate database type is selected according to the data volume and query requirements. The storage operation includes establishing a database connection, constructing an SQL insert statement (for relational databases) or calling a database API (for non-relational databases), and writing the data into a pre-created data table or data collection. To ensure data integrity, the data storage operation should have a transaction processing mechanism to ensure the atomicity of data writing. The stored TX power test data record will be used for subsequent quality traceability, data analysis, and report generation, providing data support for product quality management. After completing the data storage, the host computer program can update the user interface to display the PASS / NG result of this TX power test and feedback the storage status of the test data record to the tester, such as displaying a prompt message of "Data has been successfully saved" to complete the operation process of step S26.

[0112] Preferably, step S3 includes the following steps:

[0113] Step S31: Set the starting power and step size through the upper computer to obtain gradient scan configuration parameters;

[0114] Step S32: Set the standard transmitter power for the standard TX according to the data configured for the frequency spectrum range to obtain the current power value of the standard TX; Monitor the receiving state of the RX under test to obtain a receiving state monitoring instruction;

[0115] Step S33: Perform RX sensitivity testing on the RX under test according to the current power value of the standard TX and the receiving state monitoring instruction to obtain raw RX sensitivity data;

[0116] Step S34: Generate a sensitivity gradient curve based on the raw RX sensitivity data to obtain an RX sensitivity gradient curve;

[0117] Step S35: Store the test data of the gradient scan configuration parameters, the raw RX sensitivity data, and the RX sensitivity gradient curve to obtain an RX sensitivity test data record.

[0118] The upper computer program starts the RX sensitivity gradient scan test process. The tester configures the gradient scan parameters through the upper computer user interface. The starting transmission power is set to a relatively high power value, such as 0 dBm, to ensure that the RX under test can stably receive signals at the initial stage of the scan. The power step value is set to a small value, such as 1 dBm, to achieve fine sensitivity threshold detection. The scan termination condition can be set to the minimum transmission power, such as -100 dBm, or set to the received packet error rate (PER) reaching a preset threshold, such as 10%. These configuration parameters, including the starting transmission power, the power step value, and the scan termination condition, are integrated by the upper computer program to form a gradient scan configuration parameter set. This parameter set is stored in the upper computer memory in the form of a data structure and is called during the subsequent gradient scan process to control the transmission power adjustment of the standard TX and the scan process. The reasonable setting of the gradient scan configuration parameters directly affects the accuracy and test time of the RX sensitivity test.

[0119] The host computer program first extracts the operating frequency of the standard TX, such as 500 MHz, from the spectrum range configuration data generated in step S11. Then, the host computer encodes the starting transmit power value in the gradient scan configuration parameters, such as 0 dBm, together with the operating frequency information, into a standard TX power setting instruction. This instruction is sent to the MCU of the standard TX via the USB-to-serial module in a serial communication manner. After receiving the power setting instruction, the MCU of the standard TX controls its RF transmission circuit, sets the transmission frequency to 500 MHz, and adjusts the transmit power to 0 dBm. The power control loop of the standard TX ensures the accurate and stable transmit power. At the same time, the host computer program generates a receive status monitoring instruction and sends this instruction to the MCU of the RX under test via the USB-to-serial module in a serial communication manner. After receiving the receive status monitoring instruction, the MCU of the RX under test activates the receive status monitoring function, starts continuously monitoring the RF signal from the standard TX, and prepares to feedback the receive status data to the host computer. The current power value of the standard TX, which is set to 0 dBm, is recorded in the host computer's memory as the starting power point of the gradient scan.

[0120] After the standard TX transmits signals at the set starting power and the RX under test enters the receive status monitoring mode, the host computer program starts to execute the RX sensitivity test loop. At each power gradient point, the host computer first instructs the standard TX to maintain the current power transmission state and instructs the RX under test to continuously receive signals and collect receive status data. While receiving the signals, the MCU of the RX under test periodically measures the received signal strength indication (RSSI) value and the packet error rate (PER). The RSSI value reflects the power level of the received signal, and the PER reflects the reliability of packet reception. The RX under test sends the collected RSSI and PER data to the host computer in real time according to a predefined serial port data frame format, for example, sending a data frame containing the average RSSI value and PER value once per second. The host computer program receives and records this data to form single-point gradient test data. This data contains the receive performance indicators of the RX under test at the current transmit power of the standard TX and is part of the original RX sensitivity data. After completing the single-point data acquisition, the host computer program enters the power step attenuation control link to prepare for the test of the next power point.

[0121] After the host computer program completes the entire power gradient scanning process, that is, when the standard TX transmission power drops to the minimum value set by the scanning termination condition, or when the reception performance of the DUT RX deteriorates to the preset PER threshold, the scanning ends. At this time, the original RX sensitivity data has been accumulated in the host computer memory. This data includes the RSSI values and PER values corresponding to the DUT RX at different standard TX transmission powers. The host computer program starts the sensitivity gradient curve generation module. This module first processes and analyzes the original RX sensitivity data. For example, it calculates the average RSSI value and average PER value at each power point. Then, the program plots the RX sensitivity gradient curve with the standard TX transmission power as the horizontal axis and the average RSSI value or average PER value of the DUT RX as the vertical axis. The power - RSSI curve and power - PER curve can be generated simultaneously to evaluate the RX sensitivity characteristics from different dimensions. To more intuitively display the sensitivity threshold, the program can mark the standard TX transmission power value corresponding to when the PER value reaches the preset threshold (e.g., 10%) on the power - PER curve as a reference point for the sensitivity threshold. The generated RX sensitivity gradient curve is displayed graphically on the host computer user interface and stored in the host computer memory in the form of a data table as an important part of the RX sensitivity test results.

[0122] After the RX sensitivity gradient curve is generated, the host computer program starts the RX sensitivity test data storage module. This module integrates the key data items of this RX sensitivity gradient scanning test, including the gradient scanning configuration parameters set in step S31, the original RX sensitivity data collected in step S33 (including RSSI and PER data for all power points), and the RX sensitivity gradient curve data (curve data point set or curve image file) generated in step S34, as well as information such as the test timestamp and the unique identifier of the DUT RX (e.g., serial number SN code), to form a complete RX sensitivity test data record. The host computer program stores this data record into the local test database according to a predefined database schema or data format, such as in a structured JSON format or CSV format. The storage operation includes establishing a database connection, constructing an SQL insert statement (for relational databases) or calling a database API (for non - relational databases), and writing the data into a pre - created data table or data set. The stored RX sensitivity test data record will be used for subsequent quality traceability, data analysis, and report generation, providing data support for product reception performance evaluation. After the data storage is completed, the host computer program can update the user interface, display the RX sensitivity gradient curve graph, and feedback the storage status of the test data record to the tester, such as displaying a prompt message "RX sensitivity test data has been successfully saved" to complete the operation process of step S35.

[0123] Preferably, step S33 includes the following steps:

[0124] Step S331: Collect gradient point data for the RX under test according to the current power value of the standard TX and the received status monitoring instruction, and obtain single-point gradient test data;

[0125] Step S332: Perform power step attenuation control according to the gradient scan configuration parameters and the current power value of the standard TX to obtain the next power value of the standard TX;

[0126] Step S333: Judge the scan termination condition according to the gradient scan configuration parameters, the next power value of the standard TX, and the single-point gradient test data, and obtain a scan termination flag;

[0127] Step S334: Integrate the gradient data of the single-point gradient test data according to the scan termination flag to obtain the original RX sensitivity data.

[0128] In the embodiment of the present invention, after the standard TX stably transmits a signal according to the current power value set in step S32, the host computer program controls the data acquisition module to start. The data acquisition module instructs the RX under test to continuously receive the radio frequency signal from the standard TX and perform data sampling for a predetermined time length. For example, the continuous acquisition time is set to 2 seconds. During the data sampling, the MCU of the RX under test reads the value of the received signal strength indication (RSSI) register at a fixed sampling frequency, for example, 10 times per second, and counts the number of error packets in the received data packets for calculating the packet error rate (PER). During the 2-second sampling time, the RX under test accumulates multiple RSSI sampling values and error packet statistics data. After the data acquisition is completed, the MCU of the RX under test performs an arithmetic average on all the RSSI sampling values collected during this period to obtain an average RSSI value, and divides the counted number of error packets by the total number of transmitted packets to calculate the average PER value. The average RSSI value and the average PER value are combined into single-point gradient test data, which reflects the receiving performance level of the RX under test at the current transmission power of the standard TX and is stored in the host computer memory in the form of a data structure to provide a basis for subsequent sensitivity threshold determination.

[0129] After the host computer program completes the acquisition of single - point gradient test data, it performs a power step - down control operation. The program first reads the preset power step value, such as 1 dBm, from the gradient scan configuration parameters. Then, the program reads the current transmission power value of the standard TX from the memory. For example, the current power is - 30 dBm. The next power value of the standard TX is calculated by subtracting the power step value from the current power value. For example, - 30 dBm - 1 dBm = - 31 dBm. The calculated next power value, that is, - 31 dBm, is determined as the transmission power level that the standard TX needs to set at the next gradient scan point. The host computer program encodes this next power value, together with the operating frequency information of the standard TX, into a new standard TX power setting instruction. This instruction is sent to the MCU of the standard TX via the USB - to - serial port module in the form of serial communication to control the standard TX to adjust its transmission power to the new power value, preparing for the acquisition of test data at the next gradient point.

[0130] After the transmission power of the standard TX is adjusted to the next power value, the host computer program performs a scan termination condition judgment. The judgment basis includes the termination conditions set in the gradient scan configuration parameters and the single - point gradient test data collected in step S331. The termination condition can be set as the minimum transmission power threshold, such as - 90 dBm, or the packet error rate (PER) threshold, such as 10%. The host computer program first checks whether the next power value of the standard TX has fallen below the minimum transmission power threshold. If the next power value has fallen below the minimum transmission power threshold, the scan termination condition is met. Alternatively, the program analyzes the PER value in the single - point gradient test data collected in step S331. If the PER value has exceeded the preset PER threshold, such as exceeding 10%, the scan termination condition is also met, indicating that the receiving performance of the RX under test has deteriorated to an unacceptable level. If any of the above termination conditions is met, the host computer program generates a scan termination flag. For example, a boolean variable is set to TRUE, indicating that the gradient scan process should stop. The scan termination flag is used to control the end of the gradient scan loop.

[0131] After the host computer program completes the acquisition of single-point gradient test data for each gradient point and the judgment of the scan termination condition, it associates the acquired single-point gradient test data with the corresponding standard TX transmission power value, and integrates these data in the order of power gradient scanning to form the original RX sensitivity data. The original RX sensitivity data can be stored using data structures such as data lists or data dictionaries. For example, in a data list, each element can be a data structure containing the standard TX transmission power value, the average RSSI value, and the average PER value. In a data dictionary, the standard TX transmission power value can be used as the key, and the corresponding average RSSI value and average PER value can be used as the values. In this way, all the single-point gradient test data acquired during the entire gradient scan are effectively organized and stored, forming a complete set of original RX sensitivity data. These original data will be used in subsequent data analysis and processing steps such as the generation of sensitivity gradient curves and the determination of sensitivity thresholds, and also provide detailed data records for the traceability and analysis of test results.

[0132] Preferably, step S34 includes the following steps:

[0133] Step S341: Extract power-RSSI data pairs from the original RX sensitivity data and generate power-RSSI curve data to obtain a set of power RSSI curve data points;

[0134] Step S342: Extract power-PER / BER data pairs from the original RX sensitivity data and generate power-PER / BER curve data to obtain a set of power error rate curve data points;

[0135] Step S343: Determine the sensitivity threshold value for the set of power error rate curve data points to obtain the sensitivity threshold power value;

[0136] Step S344: Generate a sensitivity gradient curve for the set of power RSSI curve data points and the set of power error rate curve data points according to the sensitivity threshold power value, and perform threshold marking to obtain the RX sensitivity gradient curve.

[0137] In an embodiment of the present invention, the host computer program receives the original RX sensitivity data integrated in step S334. This data is stored in a structured form and contains multiple data points. Each data point corresponds to a standard TX transmit power value, as well as the average RSSI value and the average PER value measured by the RX under test at this power. In order to generate a power-RSSI curve, the data processing module first extracts the "standard TX transmit power values" of all data points and the corresponding "average RSSI values" from the original RX sensitivity data to form a series of power-RSSI data pairs. For example, if the original data contains 10 power gradient points, then 10 power-RSSI data pairs are extracted. These data pairs are organized into a power RSSI curve data point set. For example, a list data structure is used, and each element in the list is a coordinate point object, containing the X coordinate (standard TX transmit power value) and the Y coordinate (average RSSI value). The power RSSI curve data point set is a set of discrete data points, used for subsequent curve plotting and data analysis, and is stored in the host computer memory.

[0138] The host computer program processes the original RX sensitivity data obtained in step S334 again. Similar to step S341, in order to generate a power-PER / BER curve, the data processing module extracts the "standard TX transmit power values" of all data points and the corresponding "average PER values" from the original RX sensitivity data to form a series of power-PER / BER data pairs. If the RX under test outputs the bit error rate (BER) instead of the packet error rate (PER) in the sensitivity test, then power-BER data pairs are extracted. The power-PER / BER data pairs are used to describe the data reception error rate of the RX under test at different transmit powers. These data pairs are organized into a power error rate curve data point set, and the data structure can also use a list. Each element in the list is a coordinate point object, containing the X coordinate (standard TX transmit power value) and the Y coordinate (average PER or BER value). The power error rate curve data point set is the basic data for generating the power error rate curve, stored in the host computer memory, and is used for curve plotting and sensitivity threshold determination.

[0139] The host computer program receives the set of power error rate curve data points generated in step S342 and activates the sensitivity threshold determination module. The sensitivity threshold determination module has a preset threshold for PER or BER. For example, the PER threshold is set to 10%. The module traverses the set of power error rate curve data points to find the data point at which the PER value first exceeds the preset threshold. The standard TX transmit power value corresponding to this data point is determined as the sensitivity threshold power value. For example, if in the power - PER curve data point set, when the transmit power is -75 dBm, the PER value is 8%, and when the transmit power is reduced to -76 dBm, the PER value becomes 12%, then the sensitivity threshold power value is determined to be -76 dBm. If there is no data point in the power error rate curve data point set where the PER value exceeds the threshold, the sensitivity threshold determination fails, or the minimum transmit power value is used as an approximate sensitivity threshold value. The sensitivity threshold power value represents the minimum received power level at which the RX under test can reliably receive signals and is a key indicator of the RX sensitivity performance. The determination result is stored in the host computer memory for subsequent threshold marking and test report generation.

[0140] The host computer program receives the set of power RSSI curve data points generated in step S341, the set of power error rate curve data points generated in step S342, and the sensitivity threshold power value determined in step S343. The curve generation module uses a graphics library, such as Matplotlib or Chart.js, to draw curve graphs respectively according to the set of power RSSI curve data points and the set of power error rate curve data points. The horizontal axis of the curve graph is the standard TX transmit power value, and the vertical axes are the average RSSI value and the average PER / BER value respectively. To visually display the sensitivity threshold in the curve graph, the curve generation module specially marks the point corresponding to the sensitivity threshold power value on the power - PER / BER curve, for example, using a red dot or a vertical dashed line for marking. The marked position corresponds to the power point when the PER / BER value reaches the preset threshold. The finally generated RX sensitivity gradient curve, including the power - RSSI curve and the power - PER / BER curve, as well as the sensitivity threshold marking, is displayed in a graphical form on the host computer user interface and can be exported as a picture file or a vector graphics file. At the same time, the curve data point set and the threshold value data are also stored in the host computer memory to form a complete RX sensitivity gradient curve test result.

[0141] As an example of the present invention, refer to Figure 2 shown, in this example, step S4 includes:

[0142] Step S41: The upper computer loads the historical test data of the environmental noise characteristic spectrum, TX power test data record, and RX sensitivity test data record to form a historical test data set;

[0143] In an embodiment of the present invention, the host computer program starts the historical test data loading module. This module first connects to the local test database, and the database type can be a relational database such as PostgreSQL, or a non-relational database such as MongoDB. The data loading module retrieves historical test data from the database according to preset data screening conditions. The screening conditions can include product model, production batch, test time range, etc. For example, it can be set to load the TX power test data records and RX sensitivity test data records of all U-segment microphone products with the model "UMic Pro" within the most recent month, as well as the environmental noise characteristic spectra collected during these tests. The retrieved historical test data includes the environmental noise characteristic spectrum data generated in step S1, the TX power test data records generated in step S2, and the RX sensitivity test data records generated in step S3. The data loading module stores these retrieved data into different data structures in the host computer memory according to the data type. For example, a list is used to store the TX power test results, a dictionary is used to store the RX sensitivity gradient curve data, and a multi-dimensional array is used to store the environmental noise characteristic spectrum data, finally forming a historical test data set for use by the subsequent performance parameter statistical analysis and self-optimization adjustment module.

[0144] Step S42: Conduct performance parameter statistical analysis on the historical test data set to obtain performance parameter statistics; conduct quality trend evaluation based on the performance parameter statistics to obtain quality trend indicators;

[0145] In an embodiment of the present invention, the host computer program receives the historical test data set loaded in step S41 and starts the performance parameter statistical analysis module. This module first conducts statistical analysis on the historical TX power test data records. For example, it calculates statistics such as the average value, standard deviation, minimum value, and maximum value of the TX power PASS rate of the historical test batches, as well as the average value and standard deviation of the measured TX signal intensity. For the RX sensitivity test data records, the statistical analysis module analyzes the average value, standard deviation, and distribution histogram of the RX sensitivity threshold values of the historical test batches. For the environmental noise characteristic spectrum data, the statistical analysis module calculates the average value and standard deviation of the average noise power in the historical test environment, as well as the statistical characteristics of the noise power in specific frequency bands. The various performance parameter statistics calculated, such as the average PASS rate, average sensitivity threshold, average noise power, etc., are stored in the host computer memory as the basic data for quality trend evaluation.

[0146] The quality trend evaluation module conducts product quality trend analysis based on performance parameter statistics. For example, for the TX power PASS rate, the module uses time series analysis methods. For instance, it calculates the moving average PASS rate for the past week and the past month, observes the trend of the PASS rate changing over time, and determines whether the quality is stable or shows a downward trend. For the RX sensitivity threshold, the module plots a control chart, such as an X-bar control chart, to monitor whether the fluctuation range of the sensitivity threshold exceeds the control limit and determines whether the RX sensitivity performance is stable. For the environmental noise level, the module analyzes the changing trend of the average power of the environmental noise over time to determine whether there are significant changes in the noise level of the test environment. The results of the quality trend evaluation, such as the downward trend of the PASS rate, the control chart alarm of the sensitivity threshold, and the upward trend of the environmental noise level, are quantified as quality trend indicators and stored in the upper computer memory for subsequent self-optimization adjustment and anomaly alarm.

[0147] Step S43: Dynamically optimize and adjust the adaptive TX power threshold according to the performance parameter statistics, quality trend indicators, and environmental noise characteristic spectrum to obtain the optimized dynamic TX power threshold;

[0148] In the embodiment of the present invention, the upper computer program receives the performance parameter statistics and quality trend indicators generated in step S42, as well as the historical environmental noise characteristic spectrum data loaded in step S41, and starts the dynamic threshold self-optimization adjustment module. This module first analyzes the TX power PASS rate trend in the quality trend indicators. If the PASS rate is lower than the preset target yield rate for a long time, for example, lower than 95%, and the quality trend indicator shows that the PASS rate is on a downward trend, it indicates that the current adaptive TX power threshold may be too high, resulting in an increase in the misjudgment rate. At this time, the self-optimization adjustment module calculates the threshold adjustment amount that needs to be reduced according to the deviation degree of the PASS rate. For example, if the PASS rate deviation is large, the threshold is reduced by 0.5 dBm. At the same time, the module analyzes the changing trend of the environmental noise level in the quality trend indicators. If the environmental noise level increases for a long time, the adaptive TX power threshold needs to be increased to compensate for the impact of the increased noise and ensure the strictness of the test. The adjustment amount is proportional to the increase amplitude of the noise. For example, when the noise increases by 1 dBm, the threshold is increased by 1 dBm. The final optimized dynamic TX power threshold is obtained by summing the current adaptive TX power threshold and the comprehensive adjustment amount calculated based on the PASS rate deviation and environmental noise change. The optimized threshold is stored in the upper computer memory for the subsequent TX power dynamic determination link and output as part of the self-optimization parameter set.

[0149] Step S44: Gradient-step optimize and adjust the gradient scan configuration parameters according to the performance parameter statistics and quality trend indicators to obtain the optimized gradient scan step value;

[0150] In the embodiments of the present invention, the host computer program receives the performance parameter statistics and quality trend indicators generated in step S42, and starts the gradient step self-optimization adjustment module. This module first analyzes the standard deviation of the RX sensitivity threshold value in the performance parameter statistics. If the standard deviation is large, it indicates that the RX sensitivity test results have high volatility, and it is necessary to reduce the power step value of the gradient scan to improve the accuracy of the sensitivity threshold detection. The reduction amplitude of the step value is proportional to the standard deviation of the sensitivity threshold value, and a minimum step value limit is set. For example, the minimum step value limit is 0.5 dBm. At the same time, the module analyzes the RX sensitivity performance stability index in the quality trend indicators. If the RX sensitivity performance is stable for a long time and has low volatility, the power step value of the gradient scan can be appropriately increased to reduce the number of scan points, shorten the test time, and improve the test efficiency. The increase amplitude of the step value is inversely proportional to the RX sensitivity performance stability index, and a maximum step value limit is set. For example, the maximum step value limit is 2 dBm. The final optimized gradient scan step value is obtained by comprehensively adjusting according to the volatility of the sensitivity threshold value and the RX performance stability. The optimized step value is stored in the host computer memory for subsequent RX sensitivity gradient scan tests and output as part of the self-optimization parameter set.

[0151] Step S45: Generate a self-optimization parameter set for the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimization test parameter set;

[0152] In the embodiments of the present invention, the host computer program receives the optimized dynamic TX power threshold output in step S43 and the optimized gradient scan step value output in step S44. The self-optimization parameter set generation module integrates these two optimized parameters and other possibly self-optimized test parameters, such as SNR margin, receiver sensitivity compensation value, scan termination condition, etc., into a data structure to form a self-optimization test parameter set. This parameter set can be structured in JSON format or XML format, containing the names and values of each optimized parameter. For example, the JSON format self-optimization parameter set can be expressed as: `{"dynamicTxPowerThreshold":-66.5,"gradientStepValue":0.8,"snrMargin":22,"rxSensitivityCompensation":1}`. The self-optimization test parameter set represents the result of the test system's self-learning and self-optimization, containing the current optimal test parameter configuration. This parameter set is stored in the host computer memory and used to update the operating parameters of the test system, guiding the subsequent TX power test and RX sensitivity test processes. The self-optimization test parameter set is also stored in the local test database as part of the test data management, serving as a version record and traceability basis for the test parameters.

[0153] Step S46: The host computer performs structured encapsulation of the test data on the environmental noise feature spectrum, transmission calibration parameters, reception calibration parameters, TX power test data record, RX sensitivity test data record, and self-optimization test parameter set to obtain a structured test data packet;

[0154] In the embodiment of the present invention, after the host computer program completes all the test and self-optimization processes, it starts the test data structured encapsulation module. This module integrates and encapsulates various key data generated during the current test process to form a structured test data packet. The encapsulated data includes: the environmental noise feature spectrum data generated in step S1, the standard TX transmission calibration parameters and standard RX reception calibration parameters generated in step S14, the TX power test data record generated in step S2, the RX sensitivity test data record generated in step S3, and the self-optimization test parameter set generated in step S45. The structured encapsulation uses a predefined data format, for example, the JSON format or the XML format, to organize different types of data in a hierarchical structure to form a complete data packet. For example, a structured test data packet in the JSON format may include top-level fields such as "environmentNoiseProfile", "txCalibrationParameters", "rxCalibrationParameters", "txPowerTestRecord", "rxSensitivityTestRecord", "selfOptimizedParameterSet", and each field corresponds to a type of data, and its value is the specific content of that type of data. The purpose of the structured test data packet is to facilitate the storage, transmission, parsing, and reuse of test data. For example, the structured test data packet can be stored in the local file system, uploaded to the cloud database, or used to generate a test report. The structured test data packet is the final output result of test data management and is also the basis for realizing cloud integration and quality traceability of test data. After completing the data structured encapsulation, the test process ends, and the host computer program can display a test completion prompt message and feedback the storage path or upload status of the structured test data packet to the tester.

[0155] Preferably, step S43 includes the following steps:

[0156] Step S431: Read the current dynamic TX power threshold for the adaptive TX power threshold to obtain the current dynamic threshold;

[0157] Step S432: Evaluate the recent TX power test yield rate for the performance parameter statistic to obtain the recent TX yield rate;

[0158] Step S433: Calculate the threshold adjustment amount based on the yield deviation for the recent TX yield and the preset yield target value to obtain the yield deviation adjustment amount;

[0159] Step S434: Obtain the recent environmental noise level change for the quality trend indicator and the environmental noise characteristic spectrum to get the recent noise change amount;

[0160] Step S435: Calculate the threshold adjustment amount based on the noise change according to the recent noise change amount to obtain the noise change adjustment amount;

[0161] Step S436: Calculate the comprehensive adjustment amount for the yield deviation adjustment amount and the noise change adjustment amount to obtain the total adjustment amount;

[0162] Step S438: Adjust the dynamic TX power threshold according to the total adjustment amount and the current dynamic threshold to obtain the optimized dynamic TX power threshold.

[0163] In the embodiment of the present invention, after the host computer program starts the dynamic threshold self-optimization adjustment process, it first performs the operation of reading the current dynamic TX power threshold. The program accesses the memory, and the memory can be a configuration file local to the host computer, a memory variable, or a database record. The memory stores the adaptive TX power threshold currently used by the test system, which was set in the previous step S43 or during system initialization. The reading operation is implemented by calling the corresponding memory access function, file reading function, or database query statement. The read current dynamic TX power threshold, for example, with a value of -67.0 dBm, is loaded into a memory variable of the host computer program. This value serves as the starting reference value for the dynamic threshold self-optimization adjustment, and subsequent threshold adjustment calculations will be based on this reference value for addition and subtraction operations. The successful reading of the current dynamic threshold is a prerequisite step for the subsequent threshold self-optimization adjustment process, ensuring the continuity and traceability of the adjustment process.

[0164] The host computer program receives the performance parameter statistic data generated in step S42. The performance parameter statistic data includes the PASS / NG result statistic information of historical TX power tests. The program extracts the yield-related data of recent TX power tests from the performance parameter statistic data. The time window of "recent" can be preset. For example, it can be set as the last 24 hours or the last 1000 completed TX power tests. The yield evaluation operation is implemented by calculating the proportion of the number of TX power test results that are PASS within the set time window to the total number of tests. For example, if among the last 1000 TX power tests, 960 test results are PASS, then the recent TX yield is calculated as 960 / 1000 = 96%. The calculated recent TX yield, expressed in the form of a percentage or ratio value, reflects the overall passing situation of recent TX power tests and provides a quantitative basis for subsequent threshold adjustment based on yield deviation. The evaluated recent TX yield data is stored in the host computer memory for subsequent steps to call.

[0165] The host computer program obtains the recent TX yield evaluated in step S432, such as 96%, and reads the preset yield target value. The yield target value is the desired PASS rate of TX power tests. For example, the yield target value is set to 98%. For the calculation operation of the threshold adjustment amount based on yield deviation, first, calculate the deviation between the recent TX yield and the yield target value. The yield deviation is calculated by subtracting the recent TX yield from the yield target value. For example, 98% - 96% = 2%. If the yield deviation is positive, it means the recent yield is lower than the target value, and the dynamic TX power threshold needs to be appropriately reduced to increase the yield. The magnitude of the threshold adjustment amount is related to the degree of yield deviation. A proportionality coefficient can be set. For example, for every 1% yield deviation, the corresponding threshold adjustment amount is -0.2 dBm. Therefore, in this example, the yield deviation adjustment amount is calculated as 2% * (-0.2 dBm / %) = -0.4 dBm. If the yield deviation is negative or close to zero, then the yield deviation adjustment amount is zero or a small positive value, indicating that no adjustment is needed or the threshold needs to be slightly increased. The calculated yield deviation adjustment amount, in units of dBm, reflects the threshold adjustment amplitude based on yield feedback and is stored in the host computer memory.

[0166] The host computer program receives the quality trend index data generated in step S42. The quality trend index data includes the long-term change trend information of the environmental noise level and the historical environmental noise characteristic spectrum data loaded in step S41. For the operation of obtaining the change in the recent environmental noise level, analyze the time series data of the environmental noise average power in the quality trend index. The program can calculate the average value of the environmental noise average power in a recent period, such as the most recent week or the most recent day. At the same time, the program also reads the average value of the environmental noise average power in an earlier period, such as the previous week or the previous day. The change in the recent environmental noise level is obtained by calculating the difference between the average values of the environmental noise average power in these two periods. For example, if the average noise power in the most recent week is -92 dBm and the average noise power in the previous week is -95 dBm, then the recent noise change is calculated as -92 dBm - (-95 dBm) = +3 dBm, indicating that the recent environmental noise level has increased by 3 dBm. The calculated recent noise change, in units of dBm, reflects the fluctuation range of the recent environmental noise level and is stored in the host computer memory to provide a basis for threshold adjustment based on noise changes.

[0167] The host computer program obtains the recent noise change obtained in step S434, such as +3 dBm. For the operation of calculating the threshold adjustment amount based on the noise change, determine the adjustment amplitude of the dynamic TX power threshold according to the magnitude and direction of the recent noise change. The basic principle of adjustment is: when the environmental noise level increases, the dynamic TX power threshold should be increased accordingly to maintain the strictness and reliability of the test; when the environmental noise level decreases, the dynamic TX power threshold can be appropriately reduced, but it is generally not recommended to reduce it to maintain the stability of quality control. The magnitude of the noise change adjustment amount can be proportional to the recent noise change amount. For example, the set proportionality coefficient is 1:1, that is, when the environmental noise increases by 1 dBm, the threshold increases by 1 dBm. Therefore, in this example, the noise change adjustment amount is calculated as +3 dBm * 1 = +3 dBm. The calculated noise change adjustment amount, in units of dBm, reflects the threshold adjustment amplitude based on the environmental noise change and is stored in the host computer memory.

[0168] The host computer program receives the yield deviation adjustment amount calculated in step S433, such as -0.4 dBm, and the noise change adjustment amount calculated in step S435, such as +3 dBm. Through the comprehensive adjustment amount calculation operation, the yield deviation adjustment amount and the noise change adjustment amount are combined to obtain the final total adjustment amount for updating the dynamic TX power threshold. The comprehensive method can adopt simple addition, that is, directly adding the two adjustment amounts. For example, the total adjustment amount is calculated as the yield deviation adjustment amount + the noise change adjustment amount = -0.4 dBm + 3 dBm = +2.6 dBm. A more complex comprehensive method can consider setting different weight coefficients for the two adjustment amounts. For example, a greater weight is given to the noise change adjustment amount because environmental noise is the direct and main factor affecting the dynamic threshold. The finally calculated total adjustment amount, in dBm, reflects the overall adjustment range of the dynamic TX power threshold after comprehensively considering the yield feedback and environmental noise changes, and is stored in the host computer memory.

[0169] The host computer program obtains the total adjustment amount calculated in step S436, such as +2.6 dBm, and the current dynamic TX power threshold read in step S431, such as -67.0 dBm. Through the dynamic TX power threshold adjustment operation, the current dynamic TX power threshold and the total adjustment amount are added to obtain the optimized dynamic TX power threshold. For example, the optimized dynamic TX power threshold is calculated as the current dynamic TX power threshold + the total adjustment amount = -67.0 dBm + 2.6 dBm = -64.4 dBm. To prevent the threshold adjustment from exceeding the reasonable range, the upper and lower limit values of the dynamic TX power threshold can be preset. For example, the upper limit is -60 dBm and the lower limit is -75 dBm. If the calculated optimized threshold exceeds the preset range, it is clipped to the range. For example, if the calculated result is -59 dBm, the finally optimized threshold is taken as -60 dBm; if the calculated result is -76 dBm, the finally optimized threshold is taken as -75 dBm.

[0170] Preferably, step S44 includes the following steps:

[0171] Step S441: Evaluate the volatility of the recent RX sensitivity test data for the performance parameter statistic to obtain the recent sensitivity volatility;

[0172] Step S442: Evaluate the product RX sensitivity performance stability for the quality trend indicator to obtain the RX performance stability;

[0173] Step S443: Calculate the step value adjustment amount based on volatility according to the recent sensitivity volatility to obtain the volatility adjustment amount;

[0174] Step S444: Calculate the step value adjustment amount based on performance stability according to the RX performance stability to obtain the stability adjustment amount;

[0175] Step S445: Calculate the comprehensive step value adjustment amount by comprehensively calculating the volatility adjustment amount and the stability adjustment amount to obtain the comprehensive step value adjustment amount;

[0176] Step S446: Obtain the current step value; perform gradient scan step value adjustment according to the comprehensive step value adjustment amount and the current step value to obtain the optimized gradient scan step value.

[0177] In the embodiment of the present invention, the sensitivity threshold power value of multiple recent RX sensitivity tests is extracted from the performance parameter statistics. The time range of "recent" is set as the N recent RX sensitivity tests completed, and the value of N is determined according to the test requirements and the amount of data. For example, it is set to 20 times. For these N sensitivity threshold power values, calculate their sample standard deviation. The calculation formula of the sample standard deviation is: σ = sqrt[Σ(Xi - μ)^2 / (N - 1)], where Xi represents the sensitivity threshold power value of the i-th test, μ represents the average value of these N sensitivity threshold power values, and N is the number of tests. The calculated sample standard deviation σ, in units of dBm, quantitatively represents the fluctuation degree of the recent RX sensitivity test results. The larger the sample standard deviation value, the higher the volatility of the recent RX sensitivity test results. This sample standard deviation value is recorded as the recent sensitivity volatility evaluation result and is used for subsequent gradient scan step value self-optimization adjustment. For example, if the calculated sample standard deviation is 0.8 dBm, then this value is the recent sensitivity volatility obtained in this evaluation.

[0178] Utilize the RX sensitivity threshold value control chart data in the quality trend index. The control chart is an X-bar control chart, and its center line is the long-term average value of the historical RX sensitivity threshold power value. The upper control limit and the lower control limit are set based on the fluctuation range of the historical data. For example, the control limit width is 3 times the standard deviation. Evaluate the distribution of the recent RX sensitivity threshold power values on the control chart during the monitoring operation. If M (the value of M is preset, for example, 5) consecutive sensitivity threshold power value points of the recent period all fall within the control limits and there is no obvious trend or periodic fluctuation, then it is determined that the RX sensitivity performance is stable. The stability evaluation result is described by a qualitative index, such as "stable" or "unstable". When it is determined to be "stable", it can be considered to appropriately increase the gradient scan step value to improve the test efficiency; when it is determined to be "unstable", it is necessary to maintain or decrease the current step value to ensure the test accuracy. For example, if the threshold values of the past 5 RX sensitivity tests all fall within the control limits of the control chart and no abnormal pattern is shown on the control chart, then the evaluation result is "stable", indicating that the RX sensitivity performance shows good stability.

[0179] Dynamically adjust the power step value of the gradient scan according to the recent sensitivity volatility value evaluated in step S441. The volatility adjustment amount is calculated through a linear mapping relationship. Set a volatility threshold Th_volatility, for example, set it to 0.5 dBm. If the recent sensitivity volatility σ is less than Th_volatility, the volatility adjustment amount Δstep_volatility is calculated as a positive value, and the formula is: Δstep_volatility = K_volatility_increase * (Th_volatility - σ), where K_volatility_increase is the volatility increase coefficient, for example, set it to 0.2 dBm / dBm. In this case, it indicates that the volatility is low and the step value can be appropriately increased. If the recent sensitivity volatility σ is greater than or equal to Th_volatility, the volatility adjustment amount Δstep_volatility is calculated as a negative value or zero, and the formula is: Δstep_volatility = K_volatility_decrease * (Th_volatility - σ), where K_volatility_decrease is the volatility decrease coefficient, for example, set it to -0.3 dBm / dBm. In this case, it indicates that the volatility is high and the step value needs to be decreased or maintained. The unit of the volatility adjustment amount Δstep_volatility is dBm, and its numerical value and sign represent the amplitude and direction of the adjustment required for the step value. The calculated volatility adjustment amount is used for subsequent comprehensive step value adjustment calculations. For example, if the recent sensitivity volatility σ is 0.7 dBm, the volatility threshold Th_volatility is set to 0.5 dBm, and the volatility decrease coefficient K_volatility_decrease is set to -0.3 dBm / dBm. Since σ (0.7 dBm) is greater than Th_volatility (0.5 dBm), it indicates that the volatility is high and the step value needs to be decreased. The volatility adjustment amount Δstep_volatility is calculated as: Δstep_volatility = K_volatility_decrease * (σ - Th_volatility) = -0.3 dBm / dBm * (0.7 dBm - 0.5 dBm) = -0.06 dBm. This calculation result of -0.06 dBm means that based on the recent sensitivity volatility evaluation, the gradient scan step value should be decreased by 0.06 dBm. In another case, if the recent sensitivity volatility σ is 0.3 dBm, which is less than the volatility threshold Th_volatility (0.5 dBm), and the volatility increase coefficient K_volatility_increase is set to 0.2 dBm / dBm.At this time, the volatility adjustment amount Δstep_volatility is calculated as: Δstep_volatility = K_volatility_increase * (Th_volatility - σ) = 0.2 dBm / dBm * (0.5 dBm - 0.3 dBm) = 0.04 dBm. This calculation result of 0.04 dBm indicates that based on the recent sensitivity volatility assessment, the gradient scan step value can be increased by 0.04 dBm. The calculation result of the volatility adjustment amount is passed to the subsequent steps for comprehensively adjusting the gradient scan step value.

[0180] The operation of calculating the step value adjustment amount based on performance stability adjusts the gradient scan step value according to the RX performance stability assessment result obtained in step S442. The stability assessment result has two states: "stable" or "unstable". When the RX performance stability assessment result is "stable", it indicates that the RX sensitivity performance has shown good stability in recent tests. At this time, in order to improve the test efficiency, the gradient scan step value can be appropriately increased. The stability adjustment amount Δstep_stability is calculated as a positive value, and the formula is: Δstep_stability = Step_increase_value, where Step_increase_value is a preset fixed amount of step value increase, for example, set to 0.1 dBm. This fixed value represents the amplitude of the step value that is allowed to increase under stable performance conditions. When the RX performance stability assessment result is "unstable", it indicates that the RX sensitivity performance fluctuates or is abnormal. In order to ensure the reliability of the test results, it is necessary to maintain or decrease the gradient scan step value and not increase the step value. The stability adjustment amount Δstep_stability is set to zero in this case, that is, Δstep_stability = 0 dBm. The unit of the performance stability adjustment amount Δstep_stability is dBm. A positive value indicates the amplitude of increasing the step value, and a zero value indicates no adjustment. The calculated stability adjustment amount is used for the subsequent comprehensive step value adjustment calculation. For example, if the RX performance stability assessment result is "stable" and the preset fixed amount of step value increase Step_increase_value is 0.1 dBm, then the stability adjustment amount Δstep_stability = 0.1 dBm. If the RX performance stability assessment result is "unstable", then the stability adjustment amount Δstep_stability = 0 dBm.

[0181] In the comprehensive step value adjustment calculation operation, the volatility adjustment amount Δstep_volatility obtained in step S443 and the stability adjustment amount Δstep_stability obtained in step S444 are combined to obtain the final comprehensive step value adjustment amount Δstep_comprehensive. The comprehensive method uses a simple addition, directly adding the two adjustment amounts. The formula is: Δstep_comprehensive = Δstep_volatility + Δstep_stability. This method takes into account both the volatility of recent sensitivity test data and the RX performance stability factors to comprehensively adjust the gradient scan step value. For example, if the calculation result of the volatility adjustment amount Δstep_volatility is -0.06 dBm (indicating that the step value needs to be decreased by 0.06 dBm), and the calculation result of the stability adjustment amount Δstep_stability is 0.1 dBm (indicating that the step value can be increased by 0.1 dBm). Then the comprehensive step value adjustment amount Δstep_comprehensive = -0.06 dBm + 0.1 dBm = 0.04 dBm. The finally obtained comprehensive step value adjustment amount Δstep_comprehensive of 0.04 dBm is a positive value, indicating that after comprehensively considering volatility and stability, the gradient scan step value needs to be increased by 0.04 dBm. The comprehensive step value adjustment amount is used in the next step to update the current gradient scan step value.

[0182] Gradient scan step value adjustment operation. First, read the currently used gradient scan step value Step_current from the system configuration parameters. Then, based on the comprehensive step value adjustment amount Δstep_comprehensive calculated in step S445, adjust the current step value to obtain the optimized gradient scan step value Step_optimized. The adjustment method is to add the current step value to the comprehensive step value adjustment amount, and the formula is: Step_optimized = Step_current + Δstep_comprehensive. To prevent the step value adjustment from exceeding the reasonable range, set the minimum value Step_min and the maximum value Step_max of the gradient scan step value. For example, Step_min is set to 0.5dBm and Step_max is set to 2.0dBm. If the calculated optimized step value Step_optimized is less than Step_min, the final optimized gradient scan step value takes Step_min. If Step_optimized is greater than Step_max, the final optimized gradient scan step value takes Step_max. If Step_optimized is between Step_min and Step_max, the final optimized gradient scan step value is the calculated value Step_optimized. The final optimized gradient scan step value Step_optimized is updated to the system configuration parameters to replace the original current step value Step_current. In subsequent RX sensitivity gradient scan tests, the system will use this optimized step value for power step attenuation control to achieve self-optimized adjustment of the gradient scan step value. For example, if the current step value Step_current is 1.0dBm and the comprehensive step value adjustment amount Δstep_comprehensive is 0.04dBm, the calculated Step_optimized = 1.04dBm. Assume that the minimum step value Step_min is 0.5dBm and the maximum value Step_max is 2.0dBm. Since the calculated value of 1.04dBm falls within the preset range of 0.5dBm and 2.0dBm, the final optimized gradient scan step value Step_optimized is 1.04dBm. The host computer program updates this 1.04dBm value to the gradient scan configuration parameter set. When the system subsequently executes the subsequent RX sensitivity gradient scan test steps, it will directly call this updated gradient scan step value of 1.04dBm to control the step amplitude of the power decrease of the standard transmitter during the gradient scan.Through this step value self-optimizing adjustment mechanism, during the gradient scan test process, the scan step can be dynamically adjusted according to historical test data and product performance trends. On the premise of ensuring the test accuracy of RX sensitivity, the test efficiency can be improved as much as possible. Or when the product performance volatility increases, the step value can be automatically reduced to maintain the reliability of the test results. The optimized gradient scan step value is recorded as part of the self-optimizing test parameter set and used for subsequent test processes.

[0183] Preferably, the present invention further provides a wireless transmission and reception test system for performing the wireless transmission and reception test method as described above. The wireless transmission and reception test system includes:

[0184] A system initialization module for setting the spectrum range of the standard RX to obtain spectrum range configuration data; sampling the ambient noise according to the spectrum range configuration data, and extracting the noise characteristics to obtain an ambient noise feature vector; generating an ambient noise spectrum from the ambient noise feature vector and the spectrum range configuration data to obtain an ambient noise feature spectrum;

[0185] A transmit power adaptive test module for calculating a dynamic threshold according to the ambient noise feature spectrum and a preset signal-to-noise ratio margin parameter to obtain an adaptive TX power threshold; collecting the TX signal strength of the TX under test to obtain measured TX signal strength data; dynamically determining the measured TX signal strength data and the adaptive TX power threshold, and storing the test data to obtain a TX power test data record;

[0186] A receive sensitivity gradient scan module for setting the starting power and step through the upper computer to obtain gradient scan configuration parameters; monitoring the receive status of the RX under test to obtain a receive status monitoring instruction; performing an RX sensitivity test on the RX under test according to the receive status monitoring instruction to obtain RX sensitivity raw data; generating a sensitivity gradient curve from the RX sensitivity raw data to obtain an RX sensitivity gradient curve; storing the test data according to the RX sensitivity gradient curve to obtain an RX sensitivity test data record;

[0187] A threshold self-optimization module is used to perform statistical analysis of performance parameters on TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on the performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; perform parameter update and application on the self-optimized test parameter set to obtain a parameter update instruction; perform structured encapsulation of test data on the self-optimized test parameter set to obtain a structured test data packet; utilize the abnormal alarm information, parameter update instruction, and structured test data packet to implement wireless transmission and reception test tasks.

[0188] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0189] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A wireless transmission and reception test method, characterized in that, It includes the following steps: Step S1: Set the spectrum range of the standard RX to obtain spectrum range configuration data; sample the ambient noise according to the spectrum range configuration data, and then extract the noise characteristics to obtain the ambient noise feature vector; Generate the ambient noise spectrum from the ambient noise feature vector and the spectrum range configuration data to obtain the ambient noise feature spectrum; Step S2: Calculate the dynamic threshold according to the ambient noise feature spectrum and the preset signal-to-noise ratio margin parameter to obtain the adaptive TX power threshold; Collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; Dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record; Step S3: Set the starting power and step through the upper computer to obtain the gradient scan configuration parameters; monitor the receiving state of the RX under test to obtain the receiving state monitoring instruction; perform the RX sensitivity test on the RX under test according to the receiving state monitoring instruction to obtain the original RX sensitivity data; generate the sensitivity gradient curve according to the original RX sensitivity data to obtain the RX sensitivity gradient curve; Store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record; Step S4: Statistically analyze the performance parameters of the TX power test data record and the RX sensitivity test data record, and evaluate the quality trend to obtain the performance parameter statistic and the quality trend index; Detect the abnormal test result according to the performance parameter statistic and the quality trend index to obtain the abnormal alarm information; Dynamically self-optimize and adjust the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; Self-optimize and adjust the gradient step of the gradient scan configuration parameter to obtain the optimized gradient scan step value; generate the self-optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain the self-optimized test parameter set; update and apply the parameters of the self-optimized test parameter set to obtain the parameter update instruction; structurally encapsulate the test data of the self-optimized test parameter set to obtain the structured test data packet; Use the abnormal alarm information, the parameter update instruction and the structured test data packet to implement the wireless transmission and reception test task.

2. The wireless transmission and reception test method according to claim 1, wherein Step S1 includes the following steps: Step S11: The upper computer sets the spectrum range of the standard RX to obtain the spectrum range configuration data; Step S12: The standard RX samples the ambient noise according to the spectrum range configuration data to obtain the original noise sampling data; preprocess the original noise sampling data to obtain the preprocessed noise data; Step S13: The upper computer receives the preprocessed noise data from the standard RX, extracts the noise characteristics to obtain the ambient noise feature vector; the upper computer sends the self-calibration instruction to the standard TX and the standard RX respectively to obtain the self-calibration configuration instruction; Step S14: The standard TX performs transmitter loopback calibration according to the self-calibration configuration instruction to obtain the transmit calibration parameters; the standard RX performs receiver loopback calibration according to the self-calibration configuration instruction to obtain the receive calibration parameters; Step S15: The upper computer generates an environmental noise spectrum from the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum.

3. The wireless transmission and reception test method according to claim 1, characterized in that Step S2 includes the following steps: Step S21: The upper computer extracts the band noise floor from the environmental noise feature spectrum to obtain band noise floor data; calculates the minimum received power according to the band noise floor data and the preset signal-to-noise ratio margin parameter to obtain the minimum received power threshold; Step S22: Obtain the standard RX specification parameters; perform standard receiver sensitivity compensation according to the standard RX specification parameters to obtain the receiver sensitivity compensation value; Step S23: Invert the dynamic transmit power threshold according to the receiver sensitivity compensation value and the minimum received power threshold to obtain the adaptive TX power threshold; Step S24: The upper computer starts the TX under test to obtain a TX under test transmit instruction; the standard RX collects the signal strength according to the TX under test transmit instruction to obtain the measured TX signal strength data; Step S25: The upper computer dynamically determines the measured TX signal strength data and the adaptive TX power threshold to obtain the TX power test result; Step S26: Store the test data of the adaptive TX power threshold, the measured TX signal strength data, and the TX power test result to obtain the TX power test data record.

4. The wireless transmission and reception test method according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Set the starting power and step through the upper computer to obtain gradient scan configuration parameters; Step S32: Set the standard transmitter power of the standard TX according to the spectrum range configuration data to obtain the current power value of the standard TX; monitor the receiving state of the RX under test to obtain a receiving state monitoring instruction; Step S33: Perform an RX sensitivity test on the RX under test according to the current power value of the standard TX and the receiving state monitoring instruction to obtain the original RX sensitivity data; Step S34: Generate a sensitivity gradient curve according to the original RX sensitivity data to obtain an RX sensitivity gradient curve; Step S35: Store the test data of the gradient scan configuration parameters, the original RX sensitivity data, and the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

5. The wireless transmission and reception test method according to claim 4, characterized in that, Step S33 includes the following steps: Step S331: Collect single-point gradient test data for the RX under test according to the current power value of the standard TX and the receiving state monitoring instruction; Step S332: Control the power step attenuation according to the gradient scan configuration parameters and the current power value of the standard TX to obtain the next power value of the standard TX; Step S333: Judge the scan termination condition according to the gradient scan configuration parameters, the next power value of the standard TX, and the single-point gradient test data to obtain a scan termination flag; Step S334: Integrate the single-point gradient test data according to the scan termination flag to obtain the original RX sensitivity data.

6. The wireless transmission and reception test method according to claim 4, wherein Step S34 includes the following steps: Step S341: Extract the power-RSSI data pairs from the original RX sensitivity data and generate power-RSSI curve data to obtain a set of power RSSI curve data points; Step S342: Extract power - PER / BER data pairs from the original RX sensitivity data, and generate power - PER / BER curve data to obtain a set of power error rate curve data points; Step S343: Determine the sensitivity threshold power value by judging the set of power error rate curve data points; Step S344: Generate a sensitivity gradient curve for the set of power RSSI curve data points and the set of power error rate curve data points according to the sensitivity threshold power value, and perform threshold marking to obtain the RX sensitivity gradient curve.

7. The wireless transmission and reception test method according to claim 2, wherein Step S4 includes the following steps: Step S41: The upper computer loads historical test data for the environmental noise characteristic spectrum, TX power test data record, and RX sensitivity test data record to form a historical test data set; Step S42: Conduct statistical analysis of performance parameters on the historical test data set to obtain performance parameter statistics; evaluate the quality trend based on the performance parameter statistics to obtain a quality trend index; Step S43: Dynamically optimize and adjust the adaptive TX power threshold according to the performance parameter statistics, quality trend index, and environmental noise characteristic spectrum to obtain an optimized dynamic TX power threshold; Step S44: Optimize and adjust the gradient scan configuration parameters step by step according to the performance parameter statistics and quality trend index to obtain an optimized gradient scan step value; Step S45: Generate a self - optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self - optimized test parameter set; Step S46: The upper computer structurally encapsulates the environmental noise characteristic spectrum, transmission calibration parameters, reception calibration parameters, TX power test data record, RX sensitivity test data record, and self - optimized test parameter set to obtain a structured test data packet.

8. The wireless transmission and reception test method according to claim 7, wherein Step S43 includes the following steps: Step S431: Read the current dynamic TX power threshold for the adaptive TX power threshold to obtain the current dynamic threshold; Step S432: Evaluate the recent TX power test yield rate based on the performance parameter statistics to obtain the recent TX yield rate; Step S433: Calculate the threshold adjustment amount based on the yield rate deviation for the recent TX yield rate and the preset yield rate target value to obtain the yield rate deviation adjustment amount; Step S434: Obtain the recent environmental noise level change for the quality trend index and the environmental noise characteristic spectrum to obtain the recent noise change amount; Step S435: Calculate the threshold adjustment amount based on the noise change according to the recent noise change amount to obtain the noise change adjustment amount; Step S436: Calculate the comprehensive adjustment amount for the yield rate deviation adjustment amount and the noise change adjustment amount to obtain the total adjustment amount; Step S438: Adjust the dynamic TX power threshold according to the total adjustment amount and the current dynamic threshold to obtain the optimized dynamic TX power threshold.

9. The wireless transmission and reception test method according to claim 7, characterized in that, Step S44 includes the following steps: Step S441: Evaluate the volatility of recent RX sensitivity test data based on the performance parameter statistics to obtain the recent sensitivity volatility; Step S442: Evaluate the product RX sensitivity performance stability of the quality trend indicator to obtain the RX performance stability; Step S443: Calculate the step value adjustment amount based on volatility according to the recent sensitivity volatility to obtain the volatility adjustment amount; Step S444: Calculate the step value adjustment amount based on performance stability according to the RX performance stability to obtain the stability adjustment amount; Step S445: Calculate the comprehensive step value adjustment amount for the volatility adjustment amount and the stability adjustment amount to obtain the comprehensive step value adjustment amount; Step S446: Obtain the current step value; perform gradient scan step value adjustment according to the comprehensive step value adjustment amount and the current step value to obtain the optimized gradient scan step value.

10. A wireless transmission and reception test system, characterized in that, For implementing the wireless transmission and reception test method as described in claim 1, the wireless transmission and reception test system includes: A system initialization module, configured to set the spectrum range for the standard RX to obtain spectrum range configuration data; sample the ambient noise according to the spectrum range configuration data, extract the noise characteristics, and obtain the ambient noise feature vector; generate the ambient noise spectrum for the ambient noise feature vector and the spectrum range configuration data to obtain the ambient noise feature spectrum; A transmit power adaptive test module, configured to calculate the adaptive TX power threshold according to the ambient noise feature spectrum and the preset signal-to-noise ratio margin parameter; collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; perform dynamic determination on the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record; A receive sensitivity gradient scan module, configured to set the starting power and step through the upper computer to obtain gradient scan configuration parameters; monitor the receive state of the RX under test to obtain a receive state monitoring instruction; perform RX sensitivity testing on the RX under test according to the receive state monitoring instruction to obtain the original RX sensitivity data; generate a sensitivity gradient curve according to the original RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record; Threshold self-optimization module, which is used to perform statistical analysis of performance parameters on TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on the performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; perform parameter update and application on the self-optimized test parameter set to obtain a parameter update instruction; perform structured encapsulation of test data on the self-optimized test parameter set to obtain a structured test data packet; use the abnormal alarm information, parameter update instruction, and structured test data packet to implement wireless transmission and reception test tasks.

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